data_status

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden. It discloses that the tool returns status information (latest season/week, refresh logs, refresh-needed flag) and implies a read-only nature, but does not explicitly state that it makes no modifications, nor does it mention any prerequisites or safety considerations. The description is helpful but not fully transparent about behavioral traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, compact sentence that front-loads the core purpose ('Coverage and staleness') and lists key elements. Every part adds value with no filler or repetition, making it an excellent example of concise, well-structured documentation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple status tool with no parameters and no output schema, the description covers the essential aspects: what data is shown (latest season/week, refresh logs) and the decision output (whether refresh is needed). It does not specify the output format or structure, but that is a minor gap given the straightforward nature of the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the schema provides no details to clarify. Per the rubric, a zero-parameter tool earns a baseline of 4. The description does not need to explain parameters, and it does not inadvertently add ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool reports coverage and staleness, specifically the latest season/week per core table and refresh status. It differentiates from siblings like refresh_data or describe_warehouse, though it does not explicitly name them. The verb is implicit ('reports' or 'shows') rather than explicit, so it misses a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage: it is for checking data freshness and whether a refresh is needed. However, it provides no explicit when-to-use or when-not-to-use guidance relative to other tools like refresh_data or query_warehouse. The context is clear from the description but not spelled out.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.